Auto-Scope Software-Automated Otoscopy to Diagnose Ear Pathology

Auto-Scope 软件 - 用于诊断耳部病理的自动耳镜检查

基本信息

批准号：
9790958
负责人：
Metin Nafi Gurcan
金额：
$ 19.91万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-21 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9790958
关键词：
Academy Acute Address Adverse event Affect Algorithms American Antibiotics Appearance Awareness Bacterial Antibiotic Resistance Child Childhood Cholesteatoma Clinic Clinical Clip Computer Vision Systems Computer software Computer-Assisted Image Analysis Computers Cyst Databases Devices Diagnosis Diagnostic Disease Ear Ear Diseases Financial Hardship Goals Guidelines Hair Hand Health Health Care Costs Human Image Image Analysis Image Enhancement Interobserver Variability Label Language Delays Language Development Lighting Liquid substance Machine Learning Methods Mission National Institute on Deafness and Other Communication Disorders Nose Nurse Practitioners Operative Surgical Procedures Oral Otitis Media Otitis Media with Effusion Otolaryngologist Otoscopes Otoscopy Pathology Patients Pediatrics Perforation Performance Pharmaceutical Preparations Pharyngeal structure Physician Assistants Physicians Primary Care Physician Primary Health Care Public Health Radiology Specialty Reporting Research Resolution Retrieval Side Skin Societies Surgical Pathology System Testing Training Tube Tympanic membrane United States National Institutes of Health Waxes Work accurate diagnosis acute infection base central database clinical decision support cognitive development computerized diagnostic accuracy digital imaging digital video recording effusion experience hearing impairment improved middle ear novel novel strategies overtreatment personalized therapeutic primary care setting prototype software development

项目摘要

ABSTRACT Acute infections of the middle ear (acute otitis media - AOM), are the most commonly treated childhood disease. Treatment is fueled by concern for complications and effects on children's cognitive and language development. The financial burden of AOM is estimated at more than $5 billion per year. Because AOM is so common, a major societal problem is the over-diagnosis and over-treatment of this disease, as a result of two factors: First, accurately diagnosing AOM is difficult, even for experienced primary care or ear, nose, and throat (ENT) physicians. Second, with a growing shortage of primary care physicians in the US, more Nurse Practitioners and Physician Assistants serve as first-line clinicians in primary care settings, but lack extensive training in otoscopy (i.e. clinical examination of the eardrum). Consequently, practitioners often err on the side of making a diagnosis of AOM and prescribing oral antibiotics. Over 8 million unnecessary antibiotics are prescribed annually, contributing to the rise of antibiotic-resistant bacteria, and creating the largest number of pediatric medication-related adverse events. Many children with inaccurate diagnoses of AOM are referred to ENTs for surgical placement of ear tubes, and up to 70% of these cases are not indicated. Diagnosing AOM still depends on clinician subjectivity, based on a brief glimpse of the eardrum. This diagnostic subjectivity creates a critical barrier to progress in society's goal of decreasing healthcare costs and reducing over-diagnosis and over-treatment of AOM. According to the American Academy of Pediatrics in 2013, devices are needed to assist in more accurate, consistent, and objective diagnosis of AOM. A simple and objective method of analyzing an image of a patient's ear to diagnose or rule out AOM would drastically reduce over-treatment. This project will fill that gap, by developing computer-assisted image analysis (CAIA) software that provides objective information to a clinician by analyzing eardrum images collected using currently available hardware. Based on previous work in applying similar methods to improve clinician performance in radiology and surgical pathology, our overarching hypothesis is that the incremental implementation of enhanced images, automated identification of abnormalities, and retrieval of similar cases will result in improved clinician diagnostic accuracy. In our preliminary work, we developed software, called Auto-Scope, which labels eardrums as “normal” versus “abnormal.” In this study, we propose two Specific Aims to improve diagnostic performance: Specific Aim #1: Create an enhanced composite image of the eardrum. Specific Aim #2: Use machine learning approaches for clinical decision support.

抽象的中耳急性感染（急性中耳炎 - AOM）是儿童时期最常治疗的疾病对并发症以及对儿童认知和语言影响的担忧推动了治疗。 AOM 每年的财务负担估计超过 50 亿美元。常见的一个主要社会问题是对该疾病的过度诊断和过度治疗，这是由于以下两个原因造成的：因素：首先，准确诊断 AOM 很困难，即使对于经验丰富的初级保健人员或耳鼻喉科人员来说也是如此其次，随着美国初级保健医生的日益短缺，需要更多的护士。执业医师和医师助理是初级保健机构的一线服务人员，但缺乏广泛的支持耳镜检查培训（即耳膜的临床检查）。诊断 AOM 并开出超过 800 万种不必要的抗生素。每年开处方，导致抗生素耐药性细菌的增加，并创造了最大数量的抗生素耐药性细菌。许多 AOM 诊断不准确的儿童被转介给儿科药物相关的不良事件。耳鼻喉科用于手术放置耳管，其中高达 70% 的病例没有指征。诊断 AOM 仍然取决于临床医生的主观性，基于对耳膜的简短观察。诊断主观性对社会降低医疗成本目标的进展构成了关键障碍根据美国儿科学会的说法，减少 AOM 的过度诊断和过度治疗。 2013 年，需要设备来协助对 AOM 进行更准确、一致和客观的简单诊断。分析患者耳朵图像来诊断或排除 AOM 的客观方法将是戏剧性的该项目将通过开发计算机辅助图像分析（CAIA）来填补这一空白。通过分析使用收集的鼓膜图像向临床医生提供客观信息的软件基于当前可用的硬件，应用类似的方法来改进临床医生。在放射学和外科病理学的表现中，我们的总体假设是增量实施增强图像、自动识别异常、检索相似病例将提高临床医生的诊断准确性。在我们的前期工作中，我们开发了名为 Auto-Scope 的软件，该软件将耳膜标记为“正常”与“正常” “异常。”在这项研究中，我们提出了两个提高诊断性能的具体目标：具体目标#1：创建增强的鼓膜合成图像。具体目标#2：使用机器学习方法进行临床决策支持。